import random
import tqdm
import os
import re
import sys
import torch
import numpy as np
import jsonlines
import argparse
import json
from pathlib import Path
from datasets import load_from_disk,load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig

'''
python eval/evaluate_chat_gsm8k.py [--use-fewshot]
'''

INVALID_ANS = "[invalid]"
DEVICE  = "cuda:0"

def doc_to_text(doc, use_fewshot):
    if use_fewshot:
        context = "Question: Angelo and Melanie want to plan how many hours over the next week they should study together for their test next week. They have 2 chapters of their textbook to study and 4 worksheets to memorize. They figure out that they should dedicate 3 hours to each chapter of their textbook and 1.5 hours for each worksheet. If they plan to study no more than 4 hours each day, how many days should they plan to study total over the next week if they take a 10-minute break every hour, include 3 10-minute snack breaks each day, and 30 minutes for lunch each day?\nLet's think step by step\n" \
                "Angelo and Melanie think they should dedicate 3 hours to each of the 2 chapters, 3 hours x 2 chapters = 6 hours total.\nFor the worksheets they plan to dedicate 1.5 hours for each worksheet, 1.5 hours x 4 worksheets = 6 hours total.\nAngelo and Melanie need to start with planning 12 hours to study, at 4 hours a day, 12 / 4 = 3 days.\nHowever, they need to include time for breaks and lunch. Every hour they want to include a 10-minute break, so 12 total hours x 10 minutes = 120 extra minutes for breaks.\nThey also want to include 3 10-minute snack breaks, 3 x 10 minutes = 30 minutes.\nAnd they want to include 30 minutes for lunch each day, so 120 minutes for breaks + 30 minutes for snack breaks + 30 minutes for lunch = 180 minutes, or 180 / 60 minutes per hour = 3 extra hours.\nSo Angelo and Melanie want to plan 12 hours to study + 3 hours of breaks = 15 hours total.\nThey want to study no more than 4 hours each day, 15 hours / 4 hours each day = 3.75\nThey will need to plan to study 4 days to allow for all the time they need.\nThe answer is 4\n\n" \
                "Question: Mark's basketball team scores 25 2 pointers, 8 3 pointers and 10 free throws.  Their opponents score double the 2 pointers but half the 3 pointers and free throws.  What's the total number of points scored by both teams added together?\nLet's think step by step\n" \
                "Mark's team scores 25 2 pointers, meaning they scored 25*2= 50 points in 2 pointers.\nHis team also scores 6 3 pointers, meaning they scored 8*3= 24 points in 3 pointers\nThey scored 10 free throws, and free throws count as one point so they scored 10*1=10 points in free throws.\nAll together his team scored 50+24+10= 84 points\nMark's opponents scored double his team's number of 2 pointers, meaning they scored 50*2=100 points in 2 pointers.\nHis opponents scored half his team's number of 3 pointers, meaning they scored 24/2= 12 points in 3 pointers.\nThey also scored half Mark's team's points in free throws, meaning they scored 10/2=5 points in free throws.\nAll together Mark's opponents scored 100+12+5=117 points\nThe total score for the game is both team's scores added together, so it is 84+117=201 points\nThe answer is 201\n\n" \
                "Question: Bella has two times as many marbles as frisbees. She also has 20 more frisbees than deck cards. If she buys 2/5 times more of each item, what would be the total number of the items she will have if she currently has 60 marbles?\nLet's think step by step\n" \
                "When Bella buys 2/5 times more marbles, she'll have increased the number of marbles by 2/5*60 = 24\nThe total number of marbles she'll have is 60+24 = 84\nIf Bella currently has 60 marbles, and she has two times as many marbles as frisbees, she has 60/2 = 30 frisbees.\nIf Bella buys 2/5 times more frisbees, she'll have 2/5*30 = 12 more frisbees.\nThe total number of frisbees she'll have will increase to 30+12 = 42\nBella also has 20 more frisbees than deck cards, meaning she has 30-20 = 10 deck cards\nIf she buys 2/5 times more deck cards, she'll have 2/5*10 = 4 more deck cards.\nThe total number of deck cards she'll have is 10+4 = 14\nTogether, Bella will have a total of 14+42+84 = 140 items\nThe answer is 140\n\n" \
                "Question: A group of 4 fruit baskets contains 9 apples, 15 oranges, and 14 bananas in the first three baskets and 2 less of each fruit in the fourth basket. How many fruits are there?\nLet's think step by step\n" \
                "For the first three baskets, the number of apples and oranges in one basket is 9+15=24\nIn total, together with bananas, the number of fruits in one basket is 24+14=38 for the first three baskets.\nSince there are three baskets each having 38 fruits, there are 3*38=114 fruits in the first three baskets.\nThe number of apples in the fourth basket is 9-2=7\nThere are also 15-2=13 oranges in the fourth basket\nThe combined number of oranges and apples in the fourth basket is 13+7=20\nThe fourth basket also contains 14-2=12 bananas.\nIn total, the fourth basket has 20+12=32 fruits.\nThe four baskets together have 32+114=146 fruits.\nThe answer is 146\n\n" \
                f"Question: {doc['question']}\nLet's think step by step"
    else:
        context = doc['question']
    return context

def decode(tokens_list, tokenizer, raw_text_len):
    sents = []
    # print(len(tokens_list))
    for tokens in tokens_list:
        tokens = tokens.cpu().numpy().tolist()
        sent = tokenizer.tokenizer.decode(
            tokens[raw_text_len:])
        sent = sent.split('<|endoftext|>')[0]
        sent = sent.split('\n\n\n')[0]
        sent = sent.split("\n\n")[0]
        sent = sent.split("Question:")[0]
        sents.append(sent)
    return sents

def generate_sample(model, tokenizer, question):
    response, history = model.chat(
        tokenizer,
        question,
        history=None,
    )
    print(question)
    print("-------------")
    print(response)
    print("=============")
    return response


def extract_answer_hf(completion):
    def _get_last_digit(s):
        _PAT_LAST_DIGIT = re.compile(r"(?<=(\s|[\$%#{]))([+-])?(?=(\S))(0|([1-9](\d*|\d{0,2}(,\d{3})*)))?(\.\d*[1-9])?(?=(\s|[.,}]|$))")
        match = list(_PAT_LAST_DIGIT.finditer(s))
        if match:
            last_digit = match[-1].group().replace(",", "").replace("+", "")
            # print(f"The last digit in {s} is {last_digit}")
        else:
            last_digit = None
            print(f"No digits found in {s!r}")
        return last_digit

    job_gen = completion.strip('.').replace('\n', '\\n')
    last_digit = _get_last_digit(job_gen)
    if last_digit is not None:
        return eval(last_digit)
    else:
        return INVALID_ANS

def extract_answer(completion):
    try:
        last_number = re.findall(r'\d+', completion)[-1]
        return eval(last_number)
    except:
        return INVALID_ANS

def is_correct( completion, answer):
    gold = extract_answer(answer)
    assert gold != INVALID_ANS, "No ground truth answer found in the document."
    return extract_answer(completion) == gold

if __name__ == '__main__':

    parser = argparse.ArgumentParser(description='Test HF checkpoint.')
    parser.add_argument("-c", "--checkpoint-path", type=Path, help="Checkpoint path", default="Qwen/Qwen-7B-Chat")
    parser.add_argument("-f","--sample-input-file", type=str, default=None)
    parser.add_argument("-o","--sample-output-file", type=str, default="gsm8k_res.jsonl")
    parser.add_argument("--use-fewshot", action="store_true")

    args = parser.parse_args()

    if args.sample_input_file is not None:
        dataset = load_from_disk(args.sample_input_file)# or: 
    else:
        dataset = load_dataset("gsm8k", "main")

    print('Loading tokenizer ...')
    tokenizer = AutoTokenizer.from_pretrained(args.checkpoint_path, trust_remote_code=True, bf16=True, use_flash_attn=True)

    print('Loading model ...')
    model = AutoModelForCausalLM.from_pretrained(args.checkpoint_path, device_map="auto", trust_remote_code=True).eval()
    model.generation_config = GenerationConfig.from_pretrained(args.checkpoint_path, trust_remote_code=True)
    model.generation_config.do_sample = False # use greedy decoding

    test = dataset["test"]

    f_output = open(args.sample_output_file, 'w', encoding='utf-8')
    tot_length = test.num_rows
    acc_res = []
    for doc in tqdm.tqdm(test):
        context = doc_to_text(doc, args.use_fewshot)
        print(context)
        completion = generate_sample(model, tokenizer, context)
        answer = doc["answer"]
        acc = is_correct(completion, answer)
        doc["completion"] = completion
        doc["acc"] = acc
        f_output.write(json.dumps(doc, ensure_ascii=False) + "\n")
        f_output.flush()
        acc_res.append(acc)
    
    f_output.close()
    print("4-shot Acc: " if args.use_fewshot else "Zero-shot Acc", np.mean(acc_res))
